Abstract. The feedback between climate and the terrestrial carbon cycle will be a key
determinant of the dynamics of the Earth System (the thin layer that contains
and supports life) over the coming decades and centuries. However, Earth
System Model projections of the terrestrial carbon-balance vary widely over
these timescales. This is largely due to differences in their terrestrial
carbon cycle models. A major goal in biogeosciences is therefore to improve
understanding of the terrestrial carbon cycle to enable better constrained
projections. Utilising empirical data to constrain and assess component
processes in terrestrial carbon cycle models will be essential to achieving
this goal. We used a new model construction method to data-constrain all
parameters of all component processes within a global terrestrial carbon
model, employing as data constraints a collection of 12 empirical data sets
characterising global patterns of carbon stocks and flows. Our goals were to
assess the climate dependencies inferred for all component processes, assess
whether these were consistent with current knowledge and understanding,
assess the importance of different data sets and the model structure for
inferring those dependencies, assess the predictive accuracy of the model and
ultimately to identify a methodology by which alternative component models
could be compared within the same framework in the future. Although
formulated as differential equations describing carbon fluxes through plant
and soil pools, the model was fitted assuming the carbon pools were in states
of dynamic equilibrium (input rates equal output rates). Thus, the
parameterised model is of the equilibrium terrestrial carbon cycle. All but 2
of the 12 component processes to the model were inferred to have strong
climate dependencies, although it was not possible to data-constrain all
parameters, indicating some potentially redundant details. Similar climate
dependencies were obtained for most processes, whether inferred individually
from their corresponding data sets or using the full terrestrial carbon model
and all available data sets, indicating a strong overall consistency in the
information provided by different data sets under the assumed model
formulation. A notable exception was plant mortality, in which qualitatively
different climate dependencies were inferred depending on the model
formulation and data sets used, highlighting this component as the major
structural uncertainty in the model. All but two component processes
predicted empirical data better than a null model in which no climate
dependency was assumed. Equilibrium plant carbon was predicted especially
well (explaining around 70% of the variation in the withheld evaluation
data). We discuss the advantages of our approach in relation to advancing our
understanding of the carbon cycle and enabling Earth System Models to make
better constrained projections.